original_pop[2:nrow(original_pop),3:ncol(original_pop)] %>%
  ggpairs(title="경기도 시군별 인구수 및 세대수")

covid_before_after = str_remove(original_data$기준년월, "-") %>%
  as.numeric() >= 202004

data = original_data %>%
  select(c("시군명", "기준년월", "월별카드발행수량(건)", "월별카드충전액(천원)", "월별카드사용액(천원)")) %>%
  mutate(covid=if_else(covid_before_after, 'after', 'before'))


original_pop %>% colnames()

## [1] "시군구별(1)"     "시군구별(2)"     "합계(명)"        "세대수(세대)"   
## [5] "총내국인(명)"    "남내국인(명)"    "여내국인(명)"    "등록외국인 (명)"

  # 현재 총 인구 합계로 표준화
pop = original_pop %>%
  select(c("시군구별(1)", "세대수(세대)")) %>%
  rename(시군명=`시군구별(1)`, total=`세대수(세대)`)


skimr::skim(pop)

Data summary
Name	pop
Number of rows	32
Number of columns	2
_______________________
Column type frequency:
character	1
numeric	1
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
시군명	0	1	2	4	0	32	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
total	0	1	357328.2	987554.8	22060	79134	147892.5	287895.2	5717252	▇▁▁▁▁

div_data = data %>%  mutate_at(vars(-시군명, -기준년월, -covid), funs(. / `월별카드발행수량(건)` ))

## Warning: `funs()` was deprecated in dplyr 0.8.0.
## Please use a list of either functions or lambdas: 
## 
##   # Simple named list: 
##   list(mean = mean, median = median)
## 
##   # Auto named with `tibble::lst()`: 
##   tibble::lst(mean, median)
## 
##   # Using lambdas
##   list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))

# 표준화
stand_data = data %>% left_join(pop, by='시군명') %>%  mutate_at(vars(-시군명, -기준년월, -covid), funs(. / total))

데이터 개관

skimr::skim(data)

Data summary
Name	data
Number of rows	837
Number of columns	6
_______________________
Column type frequency:
character	3
numeric	3
________________________
Group variables	None

Variable type: character

skim_variable	complete_rate	min	max	n_unique
시군명	1	3	4	31
기준년월	1	7	7	27
covid	1	5	6	2

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
월별카드발행수량(건)	159	0.81	7349.39	18945.03	1	1141.25	2956	6786.5	279776	▇▁▁▁▁
월별카드충전액(천원)	141	0.83	4840481.82	6307753.76	6890	924241.25	2434881	6133970.2	56389896	▇▁▁▁▁
월별카드사용액(천원)	141	0.83	4351264.15	5615195.21	0	750687.25	2141071	5648037.2	32642111	▇▁▁▁▁

skimr::skim(stand_data)

Data summary
Name	stand_data
Number of rows	837
Number of columns	7
_______________________
Column type frequency:
character	3
numeric	4
________________________
Group variables	None

Variable type: character

skim_variable	complete_rate	min	max	n_unique
시군명	1	3	4	31
기준년월	1	7	7	27
covid	1	5	6	2

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
월별카드발행수량(건)	159	0.81	0.04	0.07	0.00	0.01	0.02	0.04	0.79	▇▁▁▁▁
월별카드충전액(천원)	141	0.83	27.56	24.82	0.02	9.11	19.30	38.87	158.70	▇▃▁▁▁
월별카드사용액(천원)	141	0.83	24.94	22.76	0.00	7.40	17.18	34.67	101.70	▇▃▂▁▁
total	0	1.00	1.00	0.00	1.00	1.00	1.00	1.00	1.00	▁▁▇▁▁

1 RCBD

rcbd_card = aov(`월별카드발행수량(건)`~시군명+covid+기준년월, data=data)
summary(rcbd_card)

##              Df    Sum Sq   Mean Sq F value   Pr(>F)    
## 시군명       27 2.603e+10 9.639e+08   4.594 6.78e-13 ***
## covid         1 1.624e+10 1.624e+10  77.405  < 2e-16 ***
## 기준년월     23 6.936e+10 3.016e+09  14.371  < 2e-16 ***
## Residuals   626 1.314e+11 2.098e+08                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 159 observations deleted due to missingness

rcbd_card_use = aov(`월별카드사용액(천원)`~시군명+covid+기준년월, data=data)
summary(rcbd_card_use)

##              Df    Sum Sq   Mean Sq F value   Pr(>F)    
## 시군명       28 9.064e+15 3.237e+14  33.164  < 2e-16 ***
## covid         1 5.738e+15 5.738e+15 587.844  < 2e-16 ***
## 기준년월     25 8.558e+14 3.423e+13   3.507 3.05e-08 ***
## Residuals   641 6.257e+15 9.761e+12                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 141 observations deleted due to missingness

rcbd_card_charge = aov(`월별카드충전액(천원)`~시군명+covid+기준년월, data=data)
summary(rcbd_card_charge)

##              Df    Sum Sq   Mean Sq F value Pr(>F)    
## 시군명       28 1.097e+16 3.918e+14  25.935 <2e-16 ***
## covid         1 6.400e+15 6.400e+15 423.693 <2e-16 ***
## 기준년월     25 5.988e+14 2.395e+13   1.586 0.0356 *  
## Residuals   641 9.683e+15 1.511e+13                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 141 observations deleted due to missingness

1.1 월별카드발행수량(건)

residuals = resid(rcbd_card)
fitted = fitted(rcbd_card)
plot(fitted, residuals, pch=20, ylim=c(-1.1,1.1)*max(abs(residuals)),
     xlab = 'fitted value', ylab='residual', main='residuals vs fitted values')
abline(0,0,lty=2)

qqnorm(residuals, datax=T, ylab='normal scores',
       xlab='residual', main='normal probability plot of the residuals')
qqline(residuals, datax = T)

1.2 월별카드사용액(천원)

residuals_use = resid(rcbd_card_use)
fitted_use = fitted(rcbd_card_use)
plot(fitted_use, residuals_use, pch=20, ylim=c(-1.1,1.1)*max(abs(residuals_use)),
     xlab = 'fitted value', ylab='residual', main='residuals vs fitted values')
abline(0,0,lty=2)

qqnorm(residuals_use, datax=T, ylab='normal scores',
       xlab='residual', main='normal probability plot of the residuals')
qqline(residuals_use, datax = T)

1.3 월별카드충전액(천원)

residuals_charge = resid(rcbd_card_charge)
fitted_charge  = fitted(rcbd_card_charge)
plot(fitted_charge , residuals_charge , pch=20, ylim=c(-1.1,1.1)*max(abs(residuals_charge )),
     xlab = 'fitted value', ylab='residual', main='residuals vs fitted values')
abline(0,0,lty=2)

qqnorm(residuals_charge , datax=T, ylab='normal scores',
       xlab='residual', main='normal probability plot of the residuals')
qqline(residuals_charge , datax = T)

1.4 div data

div_rcbd_card = aov(`월별카드발행수량(건)`~시군명+covid+기준년월, data=div_data)
summary(div_rcbd_card)

##              Df    Sum Sq   Mean Sq F value Pr(>F)
## 시군명       27 7.840e-27 2.902e-28   0.965  0.517
## covid         1 3.200e-28 3.247e-28   1.080  0.299
## 기준년월     23 6.670e-27 2.901e-28   0.965  0.510
## Residuals   626 1.882e-25 3.007e-28               
## 159 observations deleted due to missingness

div_rcbd_card_use = aov(`월별카드사용액(천원)`~시군명+covid+기준년월, data=div_data)
summary(div_rcbd_card_use)

##              Df    Sum Sq   Mean Sq F value   Pr(>F)    
## 시군명       27 8.988e+15 3.329e+14  35.117  < 2e-16 ***
## covid         1 5.800e+15 5.800e+15 611.856  < 2e-16 ***
## 기준년월     23 9.930e+14 4.317e+13   4.554 2.68e-11 ***
## Residuals   617 5.849e+15 9.479e+12                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 168 observations deleted due to missingness

div_rcbd_card_charge = aov(`월별카드충전액(천원)`~시군명+covid+기준년월, data=div_data)
summary(div_rcbd_card_charge)

##              Df    Sum Sq   Mean Sq F value   Pr(>F)    
## 시군명       27 1.082e+16 4.006e+14  29.784  < 2e-16 ***
## covid         1 6.382e+15 6.382e+15 474.509  < 2e-16 ***
## 기준년월     23 7.139e+14 3.104e+13   2.308 0.000535 ***
## Residuals   617 8.298e+15 1.345e+13                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 168 observations deleted due to missingness

1.5 월별카드사용액(천원)

div_residuals_use = resid(div_rcbd_card_use)
div_fitted_use = fitted(div_rcbd_card_use)
plot(div_fitted_use, div_residuals_use, pch=20, ylim=c(-1.1,1.1)*max(abs(div_residuals_use)),
     xlab = 'fitted value', ylab='residual', main='residuals vs fitted values')
abline(0,0,lty=2)

qqnorm(div_residuals_use, datax=T, ylab='normal scores',
       xlab='residual', main='normal probability plot of the residuals')
qqline(residuals_use, datax = T)

1.6 월별카드충전액(천원)

div_residuals_charge = resid(div_rcbd_card_charge)
div_fitted_charge  = fitted(div_rcbd_card_charge)
plot(div_fitted_charge , div_residuals_charge , pch=20, ylim=c(-1.1,1.1)*max(abs(div_residuals_charge )),
     xlab = 'fitted value', ylab='residual', main='residuals vs fitted values')
abline(0,0,lty=2)

qqnorm(div_residuals_charge , datax=T, ylab='normal scores',
       xlab='residual', main='normal probability plot of the residuals')
qqline(div_residuals_charge , datax = T)

2 RCBD with standard?

leveneTest(`월별카드발행수량(건)`~시군명, data=data)

## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)  
## group  27  1.7554 0.0109 *
##       650                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

leveneTest(`월별카드사용액(천원)`~시군명, data=data)

## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value    Pr(>F)    
## group  28  28.829 < 2.2e-16 ***
##       667                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

leveneTest(`월별카드충전액(천원)`~시군명, data=data)

## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value    Pr(>F)    
## group  28  19.641 < 2.2e-16 ***
##       667                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

leveneTest(`월별카드발행수량(건)`~시군명, data=div_data)

## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group  27               
##       650

leveneTest(`월별카드사용액(천원)`~시군명, data=div_data)

## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value    Pr(>F)    
## group  27  29.511 < 2.2e-16 ***
##       641                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

leveneTest(`월별카드충전액(천원)`~시군명, data=div_data)

## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value    Pr(>F)    
## group  27  24.092 < 2.2e-16 ***
##       641                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

leveneTest(`월별카드발행수량(건)`~시군명, data=stand_data)

## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group  27  0.4645 0.9912
##       650

leveneTest(`월별카드사용액(천원)`~시군명, data=stand_data)

## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value    Pr(>F)    
## group  28  8.7485 < 2.2e-16 ***
##       667                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

leveneTest(`월별카드충전액(천원)`~시군명, data=stand_data)

## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value    Pr(>F)    
## group  28  8.6391 < 2.2e-16 ***
##       667                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

2.1 stand data

stand_rcbd_card = aov(`월별카드발행수량(건)`~시군명*covid+기준년월, data=data)
summary(stand_rcbd_card)

##               Df    Sum Sq   Mean Sq F value   Pr(>F)    
## 시군명        27 2.603e+10 9.639e+08   4.896 5.11e-14 ***
## covid          1 1.624e+10 1.624e+10  82.498  < 2e-16 ***
## 기준년월      23 6.936e+10 3.016e+09  15.317  < 2e-16 ***
## 시군명:covid  27 1.342e+10 4.972e+08   2.525 4.22e-05 ***
## Residuals    599 1.179e+11 1.969e+08                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 159 observations deleted due to missingness

stand_rcbd_card_use = aov(`월별카드사용액(천원)`~시군명*covid+기준년월, data=data)
summary(stand_rcbd_card_use)

##               Df    Sum Sq   Mean Sq F value Pr(>F)    
## 시군명        28 9.064e+15 3.237e+14  158.24 <2e-16 ***
## covid          1 5.738e+15 5.738e+15 2804.75 <2e-16 ***
## 기준년월      25 8.558e+14 3.423e+13   16.73 <2e-16 ***
## 시군명:covid  28 5.002e+15 1.787e+14   87.33 <2e-16 ***
## Residuals    613 1.254e+15 2.046e+12                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 141 observations deleted due to missingness

stand_rcbd_card_charge = aov(`월별카드충전액(천원)`~시군명*covid+기준년월, data=data)
summary(stand_rcbd_card_charge)

##               Df    Sum Sq   Mean Sq F value   Pr(>F)    
## 시군명        28 1.097e+16 3.918e+14  55.595  < 2e-16 ***
## covid          1 6.400e+15 6.400e+15 908.224  < 2e-16 ***
## 기준년월      25 5.988e+14 2.395e+13   3.399 7.81e-08 ***
## 시군명:covid  28 5.363e+15 1.915e+14  27.180  < 2e-16 ***
## Residuals    613 4.320e+15 7.047e+12                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 141 observations deleted due to missingness

2.2 월별카드발행수량(건)

stand_residuals = resid(stand_rcbd_card)
stand_fitted = fitted(stand_rcbd_card)
plot(stand_fitted, stand_residuals, pch=20, ylim=c(-1.1,1.1)*max(abs(stand_residuals)),
     xlab = 'fitted value', ylab='residual', main='residuals vs fitted values')
abline(0,0,lty=2)

qqnorm(stand_residuals, datax=T, ylab='normal scores',
       xlab='residual', main='normal probability plot of the residuals')
qqline(stand_residuals, datax = T)

2.3 월별카드사용액(천원)

stand_residuals_use = resid(stand_rcbd_card_use)
stand_fitted_use = fitted(stand_rcbd_card_use)
plot(stand_fitted_use, stand_residuals_use, pch=20, ylim=c(-1.1,1.1)*max(abs(stand_residuals_use)),
     xlab = 'fitted value', ylab='residual', main='residuals vs fitted values')
abline(0,0,lty=2)

qqnorm(stand_residuals_use, datax=T, ylab='normal scores',
       xlab='residual', main='normal probability plot of the residuals')
qqline(stand_residuals_use, datax = T)

2.4 월별카드충전액(천원)

stand_residuals_charge = resid(stand_rcbd_card_charge)
stand_fitted_charge  = fitted(stand_rcbd_card_charge)
plot(stand_fitted_charge , stand_residuals_charge , pch=20, ylim=c(-1.1,1.1)*max(abs(stand_residuals_charge)),
     xlab = 'fitted value', ylab='residual', main='residuals vs fitted values')
abline(0,0,lty=2)

qqnorm(stand_residuals_charge , datax=T, ylab='normal scores',
       xlab='residual', main='normal probability plot of the residuals')
qqline(stand_residuals_charge , datax = T)

3 Tukey-Kramer Procedure

coef = stand_rcbd_card$coefficients%>% as.data.frame()

rownames(coef)

##  [1] "(Intercept)"                "시군명고양시"              
##  [3] "시군명과천시"               "시군명광명시"              
##  [5] "시군명광주시"               "시군명구리시"              
##  [7] "시군명군포시"               "시군명남양주시"            
##  [9] "시군명동두천시"             "시군명부천시"              
## [11] "시군명수원시"               "시군명안산시"              
## [13] "시군명안성시"               "시군명안양시"              
## [15] "시군명양주시"               "시군명양평군"              
## [17] "시군명여주시"               "시군명연천군"              
## [19] "시군명오산시"               "시군명용인시"              
## [21] "시군명의왕시"               "시군명의정부시"            
## [23] "시군명이천시"               "시군명파주시"              
## [25] "시군명평택시"               "시군명포천시"              
## [27] "시군명하남시"               "시군명화성시"              
## [29] "covidbefore"                "기준년월2019-04"           
## [31] "기준년월2019-05"            "기준년월2019-06"           
## [33] "기준년월2019-07"            "기준년월2019-08"           
## [35] "기준년월2019-09"            "기준년월2019-10"           
## [37] "기준년월2019-11"            "기준년월2019-12"           
## [39] "기준년월2020-01"            "기준년월2020-02"           
## [41] "기준년월2020-03"            "기준년월2020-04"           
## [43] "기준년월2020-05"            "기준년월2020-06"           
## [45] "기준년월2020-07"            "기준년월2020-08"           
## [47] "기준년월2020-09"            "기준년월2020-10"           
## [49] "기준년월2020-11"            "기준년월2020-12"           
## [51] "기준년월2021-01"            "기준년월2021-02"           
## [53] "기준년월2021-03"            "시군명고양시:covidbefore"  
## [55] "시군명과천시:covidbefore"   "시군명광명시:covidbefore"  
## [57] "시군명광주시:covidbefore"   "시군명구리시:covidbefore"  
## [59] "시군명군포시:covidbefore"   "시군명남양주시:covidbefore"
## [61] "시군명동두천시:covidbefore" "시군명부천시:covidbefore"  
## [63] "시군명수원시:covidbefore"   "시군명안산시:covidbefore"  
## [65] "시군명안성시:covidbefore"   "시군명안양시:covidbefore"  
## [67] "시군명양주시:covidbefore"   "시군명양평군:covidbefore"  
## [69] "시군명여주시:covidbefore"   "시군명연천군:covidbefore"  
## [71] "시군명오산시:covidbefore"   "시군명용인시:covidbefore"  
## [73] "시군명의왕시:covidbefore"   "시군명의정부시:covidbefore"
## [75] "시군명이천시:covidbefore"   "시군명파주시:covidbefore"  
## [77] "시군명평택시:covidbefore"   "시군명포천시:covidbefore"  
## [79] "시군명하남시:covidbefore"   "시군명화성시:covidbefore"

a = tibble(name=rownames(coef)[str_detect(rownames(coef), ':covid')],
       coef=coef[str_detect(rownames(coef), ':covid'),]) %>%
  arrange(coef)


coef = stand_rcbd_card_use$coefficients %>% as.data.frame()

b = tibble(name=rownames(coef)[str_detect(rownames(coef), ':covid')],
       coef=coef[str_detect(rownames(coef), ':covid'),]) %>%
  arrange(coef)



coef = stand_rcbd_card_charge$coefficients %>% as.data.frame()

c = tibble(name=rownames(coef)[str_detect(rownames(coef), ':covid')],
       coef=coef[str_detect(rownames(coef), ':covid'),]) %>%
  arrange(coef)

tukey_stand_rcbd_card = TukeyHSD(x=stand_rcbd_card, '시군명', conf.level=0.95)
tukey_stand_rcbd_card_use = TukeyHSD(x=stand_rcbd_card_use, '시군명', conf.level=0.95)
tukey_stand_rcbd_card_charge = TukeyHSD(x=stand_rcbd_card_charge, '시군명', conf.level=0.95)


plot_tukey = function(tukey, str){
  rowname_vector = rownames(tukey$시군명) 
  tukey$시군명 = tukey$시군명[str_detect(rowname_vector, str),]
  print(tukey$시군명)
  plot(tukey, las=1, tcl=-.3, col="brown")
}

par(family = 'NanumGothic')

par(mar=c(3,7,3,3))


plot_tukey(tukey_stand_rcbd_card, '수원')

##                        diff         lwr         upr        p adj
## 수원시-가평군    20710.0800   5895.7597 35524.40027 8.923641e-05
## 수원시-고양시     5113.7433  -9854.0973 20081.58397 9.999796e-01
## 수원시-과천시    20207.4517   5239.6110 35175.29230 2.178021e-04
## 수원시-광명시    16814.4800   2000.1597 31628.80027 7.870744e-03
## 수원시-광주시    14746.4933   -221.3473 29714.33397 5.988209e-02
## 수원시-구리시    16860.6817   1727.7375 31993.62599 1.070741e-02
## 수원시-군포시    15929.7850    961.9444 30897.62564 2.166405e-02
## 수원시-남양주시  11738.6600  -3229.1806 26706.50064 4.120775e-01
## 수원시-동두천시  19717.1600   4749.3194 34685.00064 3.935222e-04
## 수원시-부천시     8396.7200  -6417.6003 23211.04027 9.433380e-01
## 안산시-수원시    -3049.6000 -17863.9203 11764.72027 1.000000e+00
## 안성시-수원시   -18316.3600 -33130.6803 -3502.03973 1.586231e-03
## 안양시-수원시   -13239.0767 -28206.9173  1728.76397 1.791266e-01
## 양주시-수원시   -18215.4000 -33029.7203 -3401.07973 1.775797e-03
## 양평군-수원시   -19439.1200 -34253.4403 -4624.79973 4.310255e-04
## 여주시-수원시   -19703.0400 -34517.3603 -4888.71973 3.134467e-04
## 연천군-수원시   -20674.6817 -35807.6260 -5541.73749 1.618170e-04
## 오산시-수원시   -14714.5200 -29528.8403    99.80027 5.431310e-02
## 용인시-수원시    -3661.9517 -18629.7923 11305.88897 1.000000e+00
## 의왕시-수원시   -18894.1600 -34027.1043 -3761.21575 1.294761e-03
## 의정부시-수원시 -14843.5350 -29811.3756   124.30564 5.536159e-02
## 이천시-수원시   -18320.8683 -33288.7090 -3353.02770 1.946229e-03
## 파주시-수원시   -14836.0767 -29803.9173   131.76397 5.569837e-02
## 평택시-수원시   -16180.9426 -31313.8869 -1047.99836 2.023376e-02
## 포천시-수원시   -17958.2017 -32926.0423 -2990.36103 2.884008e-03
## 하남시-수원시   -16030.4933 -30998.3340 -1062.65270 1.975410e-02
## 화성시-수원시     -557.4517 -15525.2923 14410.38897 1.000000e+00

plot_tukey(tukey_stand_rcbd_card, '연천')

##                         diff        lwr       upr        p adj
## 연천군-가평군       35.39826 -15097.546 15168.343 1.0000000000
## 연천군-고양시   -15560.93841 -30844.203  -277.674 0.0398092448
## 연천군-과천시     -467.23007 -15750.494 14816.034 1.0000000000
## 연천군-광명시    -3860.20174 -18993.146 11272.743 0.9999999603
## 연천군-광주시    -5928.18841 -21211.453  9355.076 0.9997763015
## 연천군-구리시    -3814.00000 -19258.997 11630.997 0.9999999811
## 연천군-군포시    -4744.89674 -20028.161 10538.368 0.9999970897
## 연천군-남양주시  -8936.02174 -24219.286  6347.243 0.9216108919
## 연천군-동두천시   -957.52174 -16240.786 14325.743 1.0000000000
## 연천군-부천시   -12277.96174 -27410.906  2854.983 0.3382082534
## 연천군-수원시   -20674.68174 -35807.626 -5541.737 0.0001618170
## 연천군-안산시   -17625.08174 -32758.026 -2492.137 0.0050130528
## 연천군-안성시    -2358.32174 -17491.266 12774.623 1.0000000000
## 연천군-안양시    -7435.60507 -22718.869  7847.659 0.9914892342
## 연천군-양주시    -2459.28174 -17592.226 12673.663 1.0000000000
## 연천군-양평군    -1235.56174 -16368.506 13897.383 1.0000000000
## 연천군-여주시     -971.64174 -16104.586 14161.303 1.0000000000
## 오산시-연천군     5960.16174  -9172.783 21093.106 0.9997062536
## 용인시-연천군    17012.73007   1729.466 32295.994 0.0108661281
## 의왕시-연천군     1780.52174 -13664.475 17225.518 1.0000000000
## 의정부시-연천군   5831.14674  -9452.118 21114.411 0.9998340450
## 이천시-연천군     2353.81341 -12929.451 17637.078 1.0000000000
## 파주시-연천군     5838.60507  -9444.659 21121.869 0.9998301379
## 평택시-연천군     4493.73913 -10951.257 19938.736 0.9999992662
## 포천시-연천군     2716.48007 -12566.784 17999.744 1.0000000000
## 하남시-연천군     4644.18841 -10639.076 19927.453 0.9999981452
## 화성시-연천군    20117.23007   4833.966 35400.494 0.0004006694

plot_tukey(tukey_stand_rcbd_card_use, '수원')

##                        diff         lwr        upr        p adj
## 수원시-가평군    9482238.37   7933937.6 11030539.2 3.317751e-10
## 수원시-고양시      70317.17  -1477983.6  1618618.0 1.000000e+00
## 수원시-과천시    8810220.25   7261919.5 10358521.0 3.317751e-10
## 수원시-광명시    6778280.12   5229979.3  8326580.9 3.317751e-10
## 수원시-광주시    5430159.92   3881859.1  6978460.7 3.317751e-10
## 수원시-구리시    7695368.97   6130329.3  9260408.6 3.317751e-10
## 수원시-군포시    5132314.75   3584014.0  6680615.5 3.317751e-10
## 수원시-남양주시  4280722.25   2732421.5  5829023.0 3.320545e-10
## 수원시-동두천시  9323416.79   7775116.0 10871717.6 3.317751e-10
## 수원시-부천시    1032210.71   -516090.1  2580511.5 7.644359e-01
## 수원시-성남시    3969171.83   2464493.9  5473849.7 3.320553e-10
## 안산시-수원시     404696.08  -1143604.7  1952996.9 1.000000e+00
## 안성시-수원시   -7330487.46  -8878788.3 -5782186.7 3.317751e-10
## 안양시-수원시   -6788961.62  -8337262.4 -5240660.8 3.317751e-10
## 양주시-수원시   -8045302.19  -9578041.8 -6512562.6 3.317751e-10
## 양평군-수원시   -7490324.46  -9038625.3 -5942023.7 3.317751e-10
## 여주시-수원시   -8575017.00 -10123317.8 -7026716.2 3.317751e-10
## 연천군-수원시   -9491732.40 -11056772.1 -7926692.7 3.317751e-10
## 오산시-수원시   -6580762.62  -8129063.4 -5032461.8 3.317751e-10
## 용인시-수원시   -1681266.21  -3229567.0  -132965.4 1.589822e-02
## 의왕시-수원시   -8973580.36 -10538620.0 -7408540.7 3.317751e-10
## 의정부시-수원시 -6897708.37  -8446009.2 -5349407.6 3.317751e-10
## 이천시-수원시   -7981105.12  -9529405.9 -6432804.3 3.317751e-10
## 파주시-수원시   -6474328.92  -8022629.7 -4926028.1 3.317751e-10
## 평택시-수원시   -7428701.40  -8993741.1 -5863661.7 3.317751e-10
## 포천시-수원시   -9254149.79 -10802450.6 -7705849.0 3.317751e-10
## 하남시-수원시   -4040798.29  -5589099.1 -2492497.5 3.320502e-10
## 화성시-수원시    5220244.54   3671943.7  6768545.3 3.317751e-10

plot_tukey(tukey_stand_rcbd_card_use, '연천')

##                         diff          lwr        upr        p adj
## 연천군-가평군      -9494.025  -1574533.69  1555545.6 1.000000e+00
## 연천군-고양시   -9421415.234 -10986454.90 -7856375.6 3.317751e-10
## 연천군-과천시    -681512.150  -2246551.82   883527.5 9.986718e-01
## 연천군-광명시   -2713452.275  -4278491.94 -1148412.6 6.687843e-08
## 연천군-광주시   -4061572.484  -5626612.15 -2496532.8 3.320377e-10
## 연천군-구리시   -1796363.435  -3377964.83  -214762.0 7.652191e-03
## 연천군-군포시   -4359417.650  -5924457.32 -2794378.0 3.320531e-10
## 연천군-남양주시 -5211010.150  -6776049.82 -3645970.5 3.317751e-10
## 연천군-동두천시  -168315.609  -1733355.28  1396724.1 1.000000e+00
## 연천군-부천시   -8459521.692 -10024561.36 -6894482.0 3.317751e-10
## 연천군-성남시   -5522560.572  -7044457.22 -4000663.9 3.317751e-10
## 연천군-수원시   -9491732.400 -11056772.07 -7926692.7 3.317751e-10
## 연천군-안산시   -9896428.484 -11461468.15 -8331388.8 3.317751e-10
## 연천군-안성시   -2161244.942  -3726284.61  -596205.3 1.162333e-04
## 연천군-안양시   -2702770.775  -4267810.44 -1137731.1 7.831811e-08
## 연천군-양주시   -1446430.209  -2996076.77   103216.4 1.090932e-01
## 연천군-양평군   -2001407.942  -3566447.61  -436368.3 7.438081e-04
## 연천군-여주시    -916715.400  -2481755.07   648324.3 9.251763e-01
## 오산시-연천군    2910969.775   1345930.11  4476009.4 3.553725e-09
## 용인시-연천군    7810466.192   6245426.52  9375505.9 3.317751e-10
## 의왕시-연천군     518152.043  -1063449.35  2099753.4 9.999939e-01
## 의정부시-연천군  2594024.025   1028984.36  4159063.7 3.806146e-07
## 이천시-연천군    1510627.275    -54412.39  3075666.9 7.608621e-02
## 파주시-연천군    3017403.484   1452363.82  4582443.2 9.178752e-10
## 평택시-연천군    2063031.000    481429.60  3644632.4 4.740042e-04
## 포천시-연천군     237582.609  -1327457.06  1802622.3 1.000000e+00
## 하남시-연천군    5450934.109   3885894.44  7015973.8 3.317751e-10
## 화성시-연천군   14711976.942  13146937.27 16277016.6 3.317751e-10

plot_tukey(tukey_stand_rcbd_card_use, '성남')

##                        diff         lwr      upr        p adj
## 성남시-가평군    5513066.55  4008388.64  7017744 3.317751e-10
## 성남시-고양시   -3898854.66 -5403532.57 -2394177 3.320397e-10
## 성남시-과천시    4841048.42  3336370.51  6345726 3.317764e-10
## 성남시-광명시    2809108.30  1304430.39  4313786 3.047002e-09
## 성남시-광주시    1460988.09   -43689.82  2965666 7.113015e-02
## 성남시-구리시    3726197.14  2204300.48  5248094 3.320424e-10
## 성남시-군포시    1163142.92  -341534.99  2667821 4.503250e-01
## 성남시-남양주시   311550.42 -1193127.49  1816228 1.000000e+00
## 성남시-동두천시  5354244.96  3849567.05  6858923 3.317751e-10
## 성남시-부천시   -2936961.12 -4441639.03 -1432283 6.501800e-10
## 수원시-성남시    3969171.83  2464493.92  5473850 3.320553e-10
## 안산시-성남시    4373867.91  2869190.00  5878546 3.319769e-10
## 안성시-성남시   -3361315.63 -4865993.54 -1856638 3.322228e-10
## 안양시-성남시   -2819789.80 -4324467.71 -1315112 2.608595e-09
## 양주시-성남시   -4076130.36 -5564791.14 -2587470 3.320528e-10
## 양평군-성남시   -3521152.63 -5025830.54 -2016475 3.320260e-10
## 여주시-성남시   -4605845.17 -6110523.08 -3101167 3.318189e-10
## 연천군-성남시   -5522560.57 -7044457.22 -4000664 3.317751e-10
## 오산시-성남시   -2611590.80 -4116268.71 -1106913 6.406239e-08
## 용인시-성남시    2287905.62   783227.71  3792584 7.299669e-06
## 의왕시-성남시   -5004408.53 -6526305.18 -3482512 3.317752e-10
## 의정부시-성남시 -2928536.55 -4433214.46 -1423859 6.993404e-10
## 이천시-성남시   -4011933.30 -5516611.21 -2507255 3.320610e-10
## 파주시-성남시   -2505157.09 -4009835.00 -1000479 3.222046e-07
## 평택시-성남시   -3459529.57 -4981426.22 -1937633 3.321003e-10
## 포천시-성남시   -5284977.96 -6789655.87 -3780300 3.317751e-10
## 하남시-성남시     -71626.46 -1576304.37  1433051 1.000000e+00
## 화성시-성남시    9189416.37  7684738.46 10694094 3.317751e-10

plot_tukey(tukey_stand_rcbd_card_charge, '수원')

##                        diff       lwr        upr        p adj
## 수원시-가평군    10957886.5   8084179 13831594.4 3.317751e-10
## 수원시-고양시      477929.6  -2395778  3351637.5 1.000000e+00
## 수원시-과천시    10193149.0   7319441 13066856.9 3.317751e-10
## 수원시-광명시     7895593.6   5021886 10769301.5 3.320437e-10
## 수원시-광주시     6581617.8   3707910  9455325.7 3.320707e-10
## 수원시-구리시     8978232.9   6073457 11883008.9 3.317994e-10
## 수원시-군포시     6226485.2   3352777  9100193.2 3.331129e-10
## 수원시-남양주시   5193745.5   2320038  8067453.4 1.197980e-08
## 수원시-동두천시  10790583.9   7916876 13664291.8 3.317751e-10
## 수원시-부천시     1709122.1  -1164586  4582830.0 9.126072e-01
## 수원시-성남시     4288564.8   1495823  7081307.0 5.331953e-06
## 안산시-수원시     -132158.5  -3005866  2741549.5 1.000000e+00
## 안성시-수원시    -8583520.7 -11457229 -5709812.8 3.318819e-10
## 안양시-수원시    -7829529.0 -10703237 -4955821.0 3.320597e-10
## 양주시-수원시    -9365922.2 -12210748 -6521096.5 3.317752e-10
## 양평군-수원시    -8848627.7 -11722336 -5974919.8 3.318041e-10
## 여주시-수원시    -9988836.4 -12862544 -7115128.5 3.317751e-10
## 연천군-수원시   -10969115.3 -13873891 -8064339.4 3.317751e-10
## 오산시-수원시    -7744098.6 -10617807 -4870390.7 3.320535e-10
## 용인시-수원시    -2019715.5  -4893423   853992.4 6.632813e-01
## 의왕시-수원시   -10321026.6 -13225803 -7416250.6 3.317751e-10
## 의정부시-수원시  -7983010.9 -10856719 -5109303.0 3.320444e-10
## 이천시-수원시    -9260602.6 -12134311 -6386894.7 3.317763e-10
## 파주시-수원시    -7632338.2 -10506046 -4758630.3 3.320338e-10
## 평택시-수원시    -8550307.1 -11455083 -5645531.2 3.319314e-10
## 포천시-수원시   -10665737.9 -13539446 -7792030.0 3.317751e-10
## 하남시-수원시    -4697028.9  -7570737 -1823321.0 6.348474e-07
## 화성시-수원시     4712960.6   1839253  7586668.5 5.615003e-07

plot_tukey(tukey_stand_rcbd_card_charge, '연천')

##                         diff          lwr        upr        p adj
## 연천군-가평군      -11228.85  -2916004.79  2893547.1 1.000000e+00
## 연천군-고양시   -10491185.77 -13395961.70 -7586409.8 3.317751e-10
## 연천군-과천시     -775966.35  -3680742.29  2128809.6 9.999999e-01
## 연천군-광명시    -3073521.72  -5978297.66  -168745.8 2.340800e-02
## 연천군-광주시    -4387497.56  -7292273.49 -1482721.6 8.991586e-06
## 연천군-구리시    -1990882.43  -4926397.60   944632.7 7.336172e-01
## 연천군-군포시    -4742630.10  -7647406.04 -1837854.2 6.603644e-07
## 연천군-남양주시  -5775369.85  -8680145.79 -2870593.9 4.561849e-10
## 연천군-동두천시   -178531.43  -3083307.37  2726244.5 1.000000e+00
## 연천군-부천시    -9259993.22 -12164769.16 -6355217.3 3.317771e-10
## 연천군-성남시    -6680550.50  -9505251.28 -3855849.7 3.320264e-10
## 연천군-수원시   -10969115.35 -13873891.29 -8064339.4 3.317751e-10
## 연천군-안산시   -10836956.89 -13741732.83 -7932181.0 3.317751e-10
## 연천군-안성시    -2385594.64  -5290370.58   519181.3 3.168145e-01
## 연천군-안양시    -3139586.39  -6044362.33  -234810.5 1.707327e-02
## 연천군-양주시    -1603193.11  -4479398.82  1273012.6 9.564891e-01
## 연천군-양평군    -2120487.60  -5025263.54   784288.3 5.811217e-01
## 연천군-여주시     -980278.93  -3885054.87  1924497.0 9.999887e-01
## 오산시-연천군     3225016.77    320240.83  6129792.7 1.118846e-02
## 용인시-연천군     8949399.85   6044623.91 11854175.8 3.318038e-10
## 의왕시-연천군      648088.78  -2287426.38  3583603.9 1.000000e+00
## 의정부시-연천군   2986104.47     81328.54  5890880.4 3.498497e-02
## 이천시-연천군     1708512.72  -1196263.21  4613288.7 9.219154e-01
## 파주시-연천군     3336777.14    432001.20  6241553.1 6.285249e-03
## 평택시-연천군     2418808.22   -516706.95  5354323.4 3.099849e-01
## 포천시-연천군      303377.43  -2601398.50  3208153.4 1.000000e+00
## 하남시-연천군     6272086.47   3367310.54  9176862.4 3.333511e-10
## 화성시-연천군    15682075.93  12777300.00 18586851.9 3.317751e-10

plot_tukey(tukey_stand_rcbd_card_charge, '성남')

##                       diff        lwr        upr        p adj
## 성남시-가평군    6669321.7  3876579.5  9462063.8 3.320342e-10
## 성남시-고양시   -3810635.3 -6603377.4 -1017893.2 1.583905e-04
## 성남시-과천시    5904584.2  3111842.0  8697326.3 3.363515e-10
## 성남시-광명시    3607028.8   814286.7  6399770.9 5.948710e-04
## 성남시-광주시    2293052.9  -499689.2  5085795.1 3.172969e-01
## 성남시-구리시    4689668.1  1864967.3  7514368.8 3.578512e-07
## 성남시-군포시    1937920.4  -854821.7  4730662.5 6.893489e-01
## 성남시-남양주시   905180.7 -1887561.5  3697922.8 9.999951e-01
## 성남시-동두천시  6502019.1  3709277.0  9294761.2 3.320358e-10
## 성남시-부천시   -2579442.7 -5372184.8   213299.4 1.212611e-01
## 수원시-성남시    4288564.8  1495822.7  7081307.0 5.331953e-06
## 안산시-성남시    4156406.4  1363664.3  6949148.5 1.414926e-05
## 안성시-성남시   -4294955.9 -7087698.0 -1502213.8 5.082532e-06
## 안양시-성남시   -3540964.1 -6333706.2  -748222.0 8.987126e-04
## 양주시-성남시   -5077357.4 -7840371.1 -2314343.7 5.915691e-09
## 양평군-성남시   -4560062.9 -7352805.0 -1767320.8 6.585358e-07
## 여주시-성남시   -5700271.6 -8493013.7 -2907529.5 3.628389e-10
## 연천군-성남시   -6680550.5 -9505251.3 -3855849.7 3.320264e-10
## 오산시-성남시   -3455533.7 -6248275.8  -662791.6 1.513036e-03
## 용인시-성남시    2268849.3  -523892.8  5061591.5 3.396641e-01
## 의왕시-성남시   -6032461.7 -8857162.5 -3207760.9 3.344764e-10
## 의정부시-성남시 -3694446.0 -6487188.1  -901703.9 3.402205e-04
## 이천시-성남시   -4972037.8 -7764779.9 -2179295.7 2.257006e-08
## 파주시-성남시   -3343773.4 -6136515.5  -551031.3 2.925045e-03
## 평택시-성남시   -4261742.3 -7086443.1 -1437041.5 9.312256e-06
## 포천시-성남시   -6377173.1 -9169915.2 -3584431.0 3.320813e-10
## 하남시-성남시    -408464.0 -3201206.1  2384278.1 1.000000e+00
## 화성시-성남시    9001525.4  6208783.3 11794267.5 3.317763e-10

산학협력 빅데이터 분석

leechungpa

2021-06-17

1 RCBD

1.1 월별카드발행수량(건)

1.2 월별카드사용액(천원)

1.3 월별카드충전액(천원)

1.4 div data

1.5 월별카드사용액(천원)

1.6 월별카드충전액(천원)

2 RCBD with standard?

2.1 stand data

2.2 월별카드발행수량(건)

2.3 월별카드사용액(천원)

2.4 월별카드충전액(천원)

3 Tukey-Kramer Procedure